Demo Data from VLDB 2026 / reviewers in the wild / expert
Sabrina K. Schaupp
dblp:425/3228
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 67% Medical and health informatics · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
consensus clustering |
0.8 | 1 | 2024 | longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types · Bioinform. 2024 |
Medical and health informatics › precision medicine
patient stratification |
0.8 | 1 | 2024 | longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types · Bioinform. 2024 |
Bioinformatics and computational biology
statistical genetics |
0.8 | 1 | 2024 | longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types · Bioinform. 2024 |
Methods — techniques the papers use, named apart from their topics
finite mixture modeling · 0.8consensus clustering · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data typesabstractSUMMARY: Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. AVAILABILITY AND IMPLEMENTATION: The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/. Jonas Hagenberg, Monika Budde, Teodora Pandeva, Ivan Kondofersky, Sabrina K. Schaupp, Fabian J. Theis, Thomas G. Schulze, Nikola S. Müller, Urs Heilbronner, Richa Batra, Janine Knauer-Arloth |
Bioinform. | 5 |